Jitendra Jonnagaddala

Coronary artery disease risk assessment from unstructured electronic health records using text mining

Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstructured clinical narratives if the data is not collected specifically for risk assessment purposes. Clinical text mining can be used to extract data related to risk factors from unstructured clinical notes. This study presents methods to extract Framingham risk factors from unstructured electronic health records using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients. We developed a rule-based system to extract risk factors: age, gender, total cholesterol, HDL-C, blood pressure, diabetes history and smoking history. The results showed that the output from the text mining system was reliable, but there was a significant amount of missing data to calculate the Framingham risk score. A systematic approach for understanding missing data was followed by implementation of imputation strategies. An analysis of the 10-year Framingham risk scores for coronary artery disease in this cohort has shown that the majority of the diabetic patients are at moderate risk of CAD.

BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID

BioC is a simple XML format for text, annotations and relations, and was developed to achieve interoperability for biomedical text processing. Following the success of BioC in BioCreative IV, the BioCreative V BioC track addressed a collaborative task to build an assistant system for BioGRID curation. In this paper, we describe the framework of the collaborative BioC task and discuss our findings based on the user survey. This track consisted of eight subtasks including gene/protein/organism named entity recognition, protein–protein/genetic interaction passage identification and annotation visualization. Using BioC as their data-sharing and communication medium, nine teams, world-wide, participated and contributed either new methods or improvements of existing tools to address different subtasks of the BioC track. Results from different teams were shared in BioC and made available to other teams as they addressed different subtasks of the track. In the end, all submitted runs were merged using a machine learning classifier to produce an optimized output. The biocurator assistant system was evaluated by four BioGRID curators in terms of practical usability. The curators’ feedback was overall positive and highlighted the user-friendly design and the convenient gene/protein curation tool based on text mining. Database URL: http://www.biocreative.org/tasks/biocreative-v/track-1-bioc/

A context-aware approach for progression tracking of medical concepts in electronic medical records

Electronic medical records (EMRs) for diabetic patients contain information about heart disease risk factors such as high blood pressure, cholesterol levels, and smoking status. Discovering the described risk factors and tracking their progression over time may support medical personnel in making clinical decisions, as well as facilitate data modeling and biomedical research. Such highly patient-specific knowledge is essential to driving the advancement of evidence-based practice, and can also help improve personalized medicine and care. One general approach for tracking the progression of diseases and their risk factors described in EMRs is to first recognize all temporal expressions, and then assign each of them to the nearest target medical concept. However, this method may not always provide the correct associations. In light of this, this work introduces a context-aware approach to assign the time attributes of the recognized risk factors by reconstructing contexts that contain more reliable temporal expressions. The evaluation results on the i2b2 test set demonstrate the efficacy of the proposed approach, which achieved an F-score of 0.897. To boost the approachs ability to process unstructured clinical text and to allow for the reproduction of the demonstrated results, a set of developed .NET libraries used to develop the system is available at https://sites.google.com/site/hongjiedai/projects/nttmuclinicalnet.

Assessing Video Games to Improve Driving Skills: A Literature Review and Observational Study

Background For individuals, especially older adults, playing video games is a promising tool for improving their driving skills. The ease of use, wide availability, and interactivity of gaming consoles make them an attractive simulation tool. Objective The objective of this study was to look at the feasibility and effects of installing video game consoles in the homes of individuals looking to improve their driving skills. Methods A systematic literature review was conducted to assess the effect of playing video games on improving driving skills. An observatory study was performed to evaluate the feasibility of using an Xbox 360 Kinect console for improving driving skills. Results Twenty–nine articles, which discuss the implementation of video games in improving driving skills were found in literature. On our study, it was found the Xbox 360 with Kinect is capable of improving physical and mental activities. Xbox Video games were introduced to engage players in physical, visual and cognitive activities including endurance, postural sway, reaction time, eyesight, eye movement, attention and concentration, difficulties with orientation, and semantic fluency. However, manual dexterity, visuo-spatial perception and binocular vision could not be addressed by these games. It was observed that Xbox Kinect (by incorporating Kinect sensor facilities) combines physical, visual and cognitive engagement of players. These results were consistent with those from the literature review. Conclusions From the research that has been carried out, we can conclude that video game consoles are a viable solution for improving user’s physical and mental state. In future we propose to carry a thorough evaluation of the effects of video games on driving skills in elderly people.

A preliminary study on automatic identification of patient smoking status in unstructured electronic health records

Identifying smoking status of patients is vital for assessing their risk for a disease. With the rapid adoption of electronic health records (EHRs), patient information is scattered across various systems in the form of structured and unstructured data. In this study, we aimed to develop a hybrid system using rule-based, unsupervised and supervised machine learning techniques to automatically identify the smoking status of patients in unstructured EHRs. In addition to traditional features, we used per-document topic model distribution weights as features in our system. We also discuss the performance of our hybrid system using different feature sets. Our preliminary results demonstrated that combining per-document topic model distribution weights with traditional features improve the overall performance of the system.

Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records.

Heart disease is the leading cause of death worldwide. Therefore, assessing the risk of its occurrence is a crucial step in predicting serious cardiac events. Identifying heart disease risk factors and tracking their progression is a preliminary step in heart disease risk assessment. A large number of studies have reported the use of risk factor data collected prospectively. Electronic health record systems are a great resource of the required risk factor data. Unfortunately, most of the valuable information on risk factor data is buried in the form of unstructured clinical notes in electronic health records. In this study, we present an information extraction system to extract related information on heart disease risk factors from unstructured clinical notes using a hybrid approach. The hybrid approach employs both machine learning and rule-based clinical text mining techniques. The developed system achieved an overall microaveraged F-score of 0.8302.

TMUNSW: Disorder Concept Recognition and Normalization in Clinical Notes for SemEval-2014 Task 7

We present our participation in Task 7 of SemEval shared task 2014. The goal of this particular task includes the identification of disorder named entities and the mapping of each disorder to a unique Unified Medical Language System concept identifier, which were referred to as Task A and Task B respectively. We participated in both of these subtasks and used YTEX as a baseline system. We further developed a supervised linear chain Conditional Random Field model based on sets of features to predict disorder mentions. To take benefit of results from both systems we merged these results. Under strict condition our best run evaluated at 0.549 F-measure for Task A and an accuracy of 0.489 for Task B on test dataset. Based on our error analysis we conclude that recall of our system can be significantly increased by adding more features to the Conditional Random Field model and by using another type of tag representation or frame matching algorithm to deal with the disjoint entity mentions.

Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion.

The rapidly increasing biomedical literature calls for the need of an automatic approach in the recognition and normalization of disease mentions in order to increase the precision and effectivity of disease based information retrieval. A variety of methods have been proposed to deal with the problem of disease named entity recognition and normalization. Among all the proposed methods, conditional random fields (CRFs) and dictionary lookup method are widely used for named entity recognition and normalization respectively. We herein developed a CRF-based model to allow automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. The dataset from the BioCreative V CDR track was used to report the performance of the developed normalization methods and compare with other existing dictionary lookup based normalization methods. The best configuration achieved an F-measure of 0.77 for the disease normalization, which outperformed the best dictionary lookup based baseline method studied in this work by an F-measure of 0.13. Database URL: https://github.com/TCRNBioinformatics/DiseaseExtract

MET network in PubMed: a text-mined network visualization and curation system

Metastasis is the dissemination of a cancer/tumor from one organ to another, and it is the most dangerous stage during cancer progression, causing more than 90% of cancer deaths. Improving the understanding of the complicated cellular mechanisms underlying metastasis requires investigations of the signaling pathways. To this end, we developed a METastasis (MET) network visualization and curation tool to assist metastasis researchers retrieve network information of interest while browsing through the large volume of studies in PubMed. MET can recognize relations among genes, cancers, tissues and organs of metastasis mentioned in the literature through text-mining techniques, and then produce a visualization of all mined relations in a metastasis network. To facilitate the curation process, MET is developed as a browser extension that allows curators to review and edit concepts and relations related to metastasis directly in PubMed. PubMed users can also view the metastatic networks integrated from the large collection of research papers directly through MET. For the BioCreative 2015 interactive track (IAT), a curation task was proposed to curate metastatic networks among PubMed abstracts. Six curators participated in the proposed task and a post-IAT task, curating 963 unique metastatic relations from 174 PubMed abstracts using MET. Database URL: http://btm.tmu.edu.tw/metastasisway

NTTMUNSW BioC modules for recognizing and normalizing species and gene/protein mentions

In recent years, the number of published biomedical articles has increased as researchers have focused on biological domains to investigate the functions of biological objects, such as genes and proteins. However, the ambiguous nature of genes and their products have rendered the literature more complex for readers and curators of molecular interaction databases. To address this challenge, a normalization technique that can link variants of biological objects to a single, standardized form was applied. In this work, we developed a species normalization module, which recognizes species names and normalizes them to NCBI Taxonomy IDs. Unlike most previous work, which ignored the prefix of a gene name that represents an abbreviation of the species name to which the gene belongs, the recognition results of our module include the prefixed species. The developed species normalization module achieved an overall F-score of 0.954 on an instance-level species normalization corpus. For gene normalization, two separate modules were respectively employed to recognize gene mentions and normalize those mentions to their Entrez Gene IDs by utilizing a multistage normalization algorithm developed for processing full-text articles. All of the developed modules are BioC-compatible .NET framework libraries and are publicly available from the NuGet gallery. Database URL: https://sites.google.com/site/hjdairesearch/Projects/isn-corpus